{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
       "0          2       3  12669   9656     7561     214              2674   \n",
       "1          2       3   7057   9810     9568    1762              3293   \n",
       "2          2       3   6353   8808     7684    2405              3516   \n",
       "3          1       3  13265   1196     4221    6404               507   \n",
       "4          2       3  22615   5410     7198    3915              1777   \n",
       "..       ...     ...    ...    ...      ...     ...               ...   \n",
       "435        1       3  29703  12051    16027   13135               182   \n",
       "436        1       3  39228   1431      764    4510                93   \n",
       "437        2       3  14531  15488    30243     437             14841   \n",
       "438        1       3  10290   1981     2232    1038               168   \n",
       "439        1       3   2787   1698     2510      65               477   \n",
       "\n",
       "     Delicassen  \n",
       "0          1338  \n",
       "1          1776  \n",
       "2          7844  \n",
       "3          1788  \n",
       "4          5185  \n",
       "..          ...  \n",
       "435        2204  \n",
       "436        2346  \n",
       "437        1867  \n",
       "438        2125  \n",
       "439          52  \n",
       "\n",
       "[440 rows x 8 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "data = pd.read_csv('C:/Users/lsw/Desktop/Wholesale customers data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0    12669   9656     7561     214              2674        1338\n",
       "1     7057   9810     9568    1762              3293        1776\n",
       "2     6353   8808     7684    2405              3516        7844\n",
       "3    13265   1196     4221    6404               507        1788\n",
       "4    22615   5410     7198    3915              1777        5185\n",
       "..     ...    ...      ...     ...               ...         ...\n",
       "435  29703  12051    16027   13135               182        2204\n",
       "436  39228   1431      764    4510                93        2346\n",
       "437  14531  15488    30243     437             14841        1867\n",
       "438  10290   1981     2232    1038               168        2125\n",
       "439   2787   1698     2510      65               477          52\n",
       "\n",
       "[440 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "X = data.iloc[:,2:]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70833271, 0.53987376, 0.42274083, 0.01196489, 0.14950522,\n",
       "        0.07480852],\n",
       "       [0.44219826, 0.61470384, 0.59953989, 0.11040858, 0.20634248,\n",
       "        0.11128583],\n",
       "       [0.39655169, 0.5497918 , 0.47963217, 0.15011913, 0.2194673 ,\n",
       "        0.48961931],\n",
       "       ...,\n",
       "       [0.36446153, 0.38846468, 0.7585445 , 0.01096068, 0.37223685,\n",
       "        0.04682745],\n",
       "       [0.93773743, 0.1805304 , 0.20340427, 0.09459392, 0.01531   ,\n",
       "        0.19365326],\n",
       "       [0.67229603, 0.40960124, 0.60547651, 0.01567967, 0.11506466,\n",
       "        0.01254374]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = Normalizer().fit(X)\n",
    "X_1 = std.transform(X)\n",
    "X_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 适用轮廓系数找到最优K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans \n",
    "from sklearn.metrics import silhouette_score #轮廓系数\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'score')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score = []\n",
    "\n",
    "for i in range(2,6):\n",
    "    cluster = KMeans(n_clusters=i,random_state=0).fit(X_1)\n",
    "    score.append(silhouette_score(X_1,cluster.labels_))\n",
    "\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.plot(range(2,6),score)\n",
    "plt.xlabel('k',fontsize = 10)\n",
    "plt.ylabel('score',fontsize = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.262553</td>\n",
       "      <td>0.488689</td>\n",
       "      <td>0.677848</td>\n",
       "      <td>0.095956</td>\n",
       "      <td>0.266153</td>\n",
       "      <td>0.100561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.907702</td>\n",
       "      <td>0.171099</td>\n",
       "      <td>0.226181</td>\n",
       "      <td>0.150525</td>\n",
       "      <td>0.049222</td>\n",
       "      <td>0.071186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.575088</td>\n",
       "      <td>0.233245</td>\n",
       "      <td>0.279252</td>\n",
       "      <td>0.625425</td>\n",
       "      <td>0.053748</td>\n",
       "      <td>0.109052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "0  0.262553  0.488689  0.677848  0.095956          0.266153    0.100561\n",
       "1  0.907702  0.171099  0.226181  0.150525          0.049222    0.071186\n",
       "2  0.575088  0.233245  0.279252  0.625425          0.053748    0.109052"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = KMeans(n_clusters=3,random_state=0).fit(X_1)\n",
    "cluster.cluster_centers_\n",
    "Y =pd.DataFrame(data=cluster.cluster_centers_,columns=data.columns[2:])\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-62-1b0e7061760d>:6: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  plt.pie(d.values,\n",
      "<ipython-input-62-1b0e7061760d>:9: MatplotlibDeprecationWarning: Unrecognized location 'd.columns'. Falling back on 'best'; valid locations are\n",
      "\tbest\n",
      "\tupper right\n",
      "\tupper left\n",
      "\tlower left\n",
      "\tlower right\n",
      "\tright\n",
      "\tcenter left\n",
      "\tcenter right\n",
      "\tlower center\n",
      "\tupper center\n",
      "\tcenter\n",
      "This will raise an exception in 3.3.\n",
      "  plt.legend(loc = 'd.columns')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x142765b8940>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = cluster.labels_\n",
    "b = pd.Series(a)\n",
    "from collections import Counter\n",
    "c = dict(Counter(b))\n",
    "d = pd.DataFrame([c])\n",
    "plt.pie(d.values,\n",
    "        labels=d.columns,\n",
    "       autopct='%1.0f%%')\n",
    "plt.legend(loc = 'd.columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y.plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
